Suppose the geo-marketplace has locations of many users, where each user's location can be sold at different levels of accuracy corresponding to her privacy vs. price preferences.
There are different approaches to capture privacy or inaccuracy. For our purpose, and to simplify the discussion, we assume privacy or inaccuracy is captured as a data point with a Gaussian noise level represented by the standard deviation of the noise distribution. This can be simply extended to use more sophisticated location privacy mechanisms such as Geo-Indistinguishablity [1]}.
In Figure REF , for example, a user's true location at \(\mathbf {x}\)  can be sold as noisy points with mean and standard deviation \((z_1, \sigma _1)\)  for \(\$1\)  or \((z_2, \sigma _2)\)  for \(\$2\) .
